We consider the problem of secure distributed matrix multiplication (SDMM) in which a user wishes to compute the product of two matrices with the assistance of honest but curious servers. We construct ...

The forward pass in a neural network can be significantly accelerated using nvmath-python. By executing the RELU_BIAS epilog, users can perform matrix multiplication, add biases, and apply ReLU ...

Optimizing Neural Network Passes The forward pass in a neural network can be significantly accelerated using nvmath-python. By executing the RELU_BIAS epilog, users can perform matrix multiplication, ...

Optimizing Neural Network Passes The forward pass in a neural network can be significantly accelerated using nvmath-python. By executing the RELU_BIAS epilog, users can perform matrix multiplication, ...

Using NumPy for array and matrix math in Python Many mathematical operations, especially in machine learning or data science, involve working with matrixes, or lists of numbers. The naive way to ...

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In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery ...

Here, we profiled MCF-7 cells with paired RNA-seq and ATAC-seq measurements after we exposed them to retinoic acid, TGF-β, and both signals. We found that while genes’ transcriptional responses ...